
#
# myloss.py : implementation of the Dice coeff and the associated loss
#

from math import exp

from torch.autograd import Variable

import numpy as np
import torch.nn.functional as F
from utils.saveNet import *

def gaussian_nd(shape, sigma=1.0, mu=0.0):
    """ create a n dimensional gaussian kernel for the given shape """
    m = np.meshgrid(*[np.linspace(-1,1,s) for s in shape])
    d = np.sqrt(np.sum([x*x for x in m], axis=0))
    g = np.exp(-( (d-mu)**2 / ( 2.0 * sigma**2 ) ) )
    return g / np.sum(g)

def create_NDwindow(window_shape,channel):
    _nD_window = torch.Tensor(gaussian_nd(window_shape))
    window = Variable(_nD_window.expand(channel, 1, *window_shape).contiguous())
    return window



def gaussian(window_size, sigma):
    gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
    return gauss/gauss.sum()


def create_window(window_size, channel):
    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
    window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
    return window



def _ssim(img1, img2, window, window_size, channel, size_average=True, full=False):
    padd = 0

    mu1 = F.conv2d(img1, window, padding=padd, groups=channel)
    mu2 = F.conv2d(img2, window, padding=padd, groups=channel)

    mu1_sq = mu1.pow(2)
    mu2_sq = mu2.pow(2)
    mu1_mu2 = mu1*mu2

    sigma1_sq = F.conv2d(img1*img1, window, padding=padd, groups=channel) - mu1_sq
    sigma2_sq = F.conv2d(img2*img2, window, padding=padd, groups=channel) - mu2_sq
    sigma12 = F.conv2d(img1*img2, window, padding=padd, groups=channel) - mu1_mu2

    C1 = 0.01**2
    C2 = 0.03**2

    ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2))

    v1 = 2.0 * sigma12 + C2
    v2 = sigma1_sq + sigma2_sq + C2
    cs = torch.mean(v1 / v2)

    if size_average:
        ret = ssim_map.mean()
    else:
        ret = ssim_map.mean(1).mean(1).mean(1)

    if full:
        return ret, cs
    return ret


class SSIM(torch.nn.Module):
    def __init__(self, window_size=11, size_average=True):
        super(SSIM, self).__init__()
        self.window_size = window_size
        self.size_average = size_average
        self.channel = 1
        self.window = create_window(window_size, self.channel)

    def forward(self, img1, img2):
    
        channel = img1.shape[1]

        if channel == self.channel and self.window.data.type() == img1.data.type():
            window = self.window
        else:
            window = create_window(self.window_size, channel)
            
            if img1.is_cuda:
                window = window.cuda(img1.get_device())
            window = window.type_as(img1)
            
            self.window = window
            self.channel = channel

        return _ssim(img1, img2, window, self.window_size, channel, self.size_average)




def _ssim_3d(img1, img2, window, window_size, channel, size_average=True, full=False):
    padd = 0

    mu1 = F.conv3d(img1, window, padding=padd, groups=channel)
    mu2 = F.conv3d(img2, window, padding=padd, groups=channel)

    mu1_sq = mu1.pow(2)
    mu2_sq = mu2.pow(2)
    mu1_mu2 = mu1*mu2

    sigma1_sq = F.conv3d(img1*img1, window, padding=padd, groups=channel) - mu1_sq
    sigma2_sq = F.conv3d(img2*img2, window, padding=padd, groups=channel) - mu2_sq
    sigma12 = F.conv3d(img1*img2, window, padding=padd, groups=channel) - mu1_mu2

    C1 = 0.01**2
    C2 = 0.03**2

    ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2))

    v1 = 2.0 * sigma12 + C2
    v2 = sigma1_sq + sigma2_sq + C2
    cs = torch.mean(v1 / v2)

    if size_average:
        ret = ssim_map.mean()
    else:
        ret = ssim_map.mean(1).mean(1).mean(1)

    if full:
        return ret, cs
    return ret

class SSIM_3D(torch.nn.Module):
    def __init__(self, window_shape=(11,11,11), size_average=True, channels=1):
        super(SSIM_3D, self).__init__()
        self.window_shape = window_shape
        self.size_average = size_average
        self.channels = channels
        self.window = create_NDwindow(window_shape, self.channels)

    def forward(self, img1, img2):
        
        self.window.to(img1.get_device())
        self.window  = self.window.type_as(img1)

        return _ssim_3d(img1, img2, self.window, self.window_shape, self.channels, self.size_average)


def ssim(img1, img2, window_size=11, size_average=True, full=False):
    (_, channel, height, width) = img1.size()

    real_size = min(window_size, height, width)
    window = create_window(real_size, channel)
    
    if img1.is_cuda:
        window = window.cuda(img1.get_device())
    window = window.type_as(img1)
    
    return _ssim(img1, img2, window, real_size, channel, size_average, full=full)


def msssim(img1, img2, window_size=11, size_average=True):
    # TODO: fix NAN results
    if img1.size() != img2.size():
        raise RuntimeError('Input images must have the same shape (%s vs. %s).' %
                           (img1.size(), img2.size()))
    if len(img1.size()) != 4:
        raise RuntimeError('Input images must have four dimensions, not %d' %
                           len(img1.size()))

    if type(img1) is not Variable or type(img2) is not Variable:
        raise RuntimeError('Input images must be Variables, not %s' % 
                            img1.__class__.__name__)

    weights = Variable(torch.FloatTensor([0.0448, 0.2856, 0.3001, 0.2363, 0.1333]))
    if img1.is_cuda:
        weights = weights.cuda(img1.get_device())

    levels = weights.size()[0]
    mssim = []
    mcs = []
    for _ in range(levels):
        sim, cs = ssim(img1, img2, window_size=window_size, size_average=size_average, full=True)
        mssim.append(sim)
        mcs.append(cs)

        img1 = F.avg_pool2d(img1, (2, 2))
        img2 = F.avg_pool2d(img2, (2, 2))

    mssim = torch.cat(mssim)
    mcs = torch.cat(mcs)
    return (torch.prod(mcs[0:levels-1] ** weights[0:levels-1]) *
            (mssim[levels-1] ** weights[levels-1]))


class MSSSIM(torch.nn.Module):
    def __init__(self, window_size=11, size_average=True, channel=3):
        super(MSSSIM, self).__init__()
        self.window_size = window_size
        self.size_average = size_average
        self.channel = channel

    def forward(self, img1, img2):
        # TODO: store window between calls if possible
        return msssim(img1, img2, window_size=self.window_size, size_average=self.size_average)


